Extraction Equilibria between Benzene and Water of Uni- and Bivalent Metal Picrates with Lariat 16-Crown-5: Effect of the Side Arms on the Extraction Ability and Selectivity

The overall extraction constants (K_ex) of uni- andbivalent metal picrates with 15-(2,5-dioxahexyl)-15-methyl-16-crown-5(L16C5) were determined between benzene and water at 25°C. TheK_ex values were analyzed into the constituent equilibriumconstants, i.e., the extraction constant of picric acid, the distributionconstant of the crown ether, the stability constant of the metalion–crown ether complex in water, and the ion-pair extraction constantof the complex cation with the picrate anion. The K_ex valuedecreases in the orders Ag⁺ > Na⁺ >Tl⁺ > K⁺ > Li⁺ andPb²⁺ > Ba²⁺ > Sr²⁺ for theuni- and bivalent metals, respectively, which are the same as those observedfor 16C5. The extraction selectivity was found to be governed by theselectivity of the ion-pair extraction of the L16C5–metal picratecomplex rather than by that of the complex formation in water. Theextraction ability of L16C5 is smaller for all the metals than that of 16C5,which is mostly attributed to the higher lipophilicity of L16C5. Differencesin the extraction selectivity between L16C5 and 16C5 were observed for thebivalent metals but little for the univalent metals. The side-arm effect onthe extraction selectivity was interpreted on the basis of the negativecorrelation between the effect on the complex stability constant in waterand that on the ion-pair extraction constant.     

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

Pseudo-Binary Phase Diagram of LiNH2-MH (M = Na,K) Eutectic Mixture

The hunt for a cleaner energy carrier leads us to consider a source that produces no toxic byproducts. One of the targeted alternatives in this approach is hydrogen energy, which, unfortunately, suffers from a lack of efficient storage media. Solid-state hydrogen absorption systems, such as lithium amide (LiNH₂) systems, may store up to 6.5 weight percent hydrogen. However, the temperature of hydrogenation and dehydrogenation is too high for practical use. Various molar ratios of LiNH₂ with sodium hydride (NaH) and potassium hydride (KH) have been explored in this paper. The temperature of hydrogenation for LiNH₂ combined with KH and NaH was found to be substantially lower than the temperature of individual LiNH₂. This lower temperature operation of both LiNH₂-NaH and LiNH₂-KH systems was investigated in depth, and the eutectic melting phenomenon was observed. Systematic thermal studies of this amide-hydride system in different compositions were carried out, which enabled the plotting of a pseudo-binary phase diagram. The occurrence of eutectic interaction increased atomic mobility, which resulted in the kinetic modification followed by an increase in the reactivity of two materials. For these eutectic compositions, i.e., 0.15LiNH₂-0.85NaH and 0.25LiNH₂-0.75KH, the lowest melting temperature was found to be 307 °C and 235 °C, respectively. Morphological studies were used to investigate and present the detailed mechanism linked with this phenomenon.     

ZnO薄膜的椭偏和DLTS特性

用射频磁控溅射在硅衬底上淀积氧化锌薄膜,并对样品分别作氮气、空气、氧气等不同条件下退火处理。为研究退火气氛对ZnO/Si薄膜中缺陷以及折射率的影响,由深能级瞬态谱(DLTS)以及椭偏测量方法进行了检测。椭偏测量结果表明相对原始生长的样品,在氮气和空气退火使ZnO薄膜折射率下降,但氧气中退火使折射率升高。我们对折射率的这种变化机理进行了解释。DLTS测量得到一个与Zni**相关的深能级中心E1存在,氧气气氛退火可以消除E1能级。在氮气退火情况下Zn*i*的存在对抑制VO引起的薄膜折射率下降有利。     

Te溶剂Bridgman法CdMnTe晶体核辐射探测器的制备和表征

碲锰镉(CdMnTe)作为性能优异的室温核辐射探测器材料,可用于环境监测和工业无损检测领域。本文中采用Te溶剂Bridgman法生长In掺杂Cd_0.9Mn_0.1Te晶体,制备成10 mm×10 mm×2 mm大小的室温单平面探测器,研究了该探测器对²⁴¹Am@59.5 keV γ射线源的能谱响应。通过表征红外透过率、电阻率以及探测器能谱响应等参数,综合评定了探测器用CdMnTe晶体的质量、电学和探测器性能。结果表明,晶片的红外透过率均在55%以上,最好可达到60%。采用湿法钝化,100 V偏压下的漏电流由钝化前的9.48 nA降为钝化后的7.90 nA,钝化后的电阻率为2.832×10¹⁰ Ω·cm。在-400 V反向偏压下,CdMnTe探测器对²⁴¹Am@59.5 keV γ射线源的能量分辨率在钝化前后分别为13.53%和12.51%,钝化后的电子迁移率寿命积为1.049×10^-3 cm²/V。研究了探测器的能量分辨率随电压的变化特性,当偏压≤400 V时,探测器的能量分辨率主要由载流子的收集效率决定,而当偏压>400 V时,能量分辨率由漏电流决定。本文研究结果表明,Te溶剂Bridgman法生长的CdMnTe晶体质量较好,电阻率和电子迁移率寿命积满足探测器制备需求。     

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.     

Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.     

玻璃中CdSeS纳米晶体的生长及其性能

对掺有镉、硒、硫的玻璃在500—800℃退火2—24h,生长了不同尺寸的CdSxSe1x纳米晶体.用分光光度计和光致发光光谱(PL)分析了纳米晶体的性能.退火温度低于550℃,纳米晶体处于成核阶段,600—625℃处于正常扩散生长阶段,700—800℃处于竞争生长阶段;而650℃处于两种生长阶段之间.虽然650℃下生长的纳米晶体的尺寸分布比较窄,但纳米晶体的尺寸随退火时间的延长几乎不变,在该温度改变退火时间很难改变纳米晶体的平均尺寸.在所有样品中出现了深能级缺陷,在650℃退火时间小于4h或大于16h有利 关键词： 纳米晶体 生长机理 深能级缺陷